Briefly describe the schema structure of a distributed database

The distributed database system is developed on the basis of the centralized database system. It is the product of the combination of database technology and network technology. What is a distributed database:

The distributed database system is developed on the basis of the centralized database system. It is the product of the combination of database technology and network technology. There are two types of distributed database systems:

One is physically distributed, but logically centralized. This kind of distributed database is only suitable for a relatively single, small unit or department. Another type of distributed database system is physically and logically distributed, which is the so-called federated distributed database system.

Since the sub-database systems that make up the federation are relatively "autonomous", this kind of system can accommodate a variety of databases with different purposes and large differences, which is more suitable for the integration of large-scale databases. Distributed Database System (DDBS) consists of Distributed Database Management System (DDBMS) and Distributed Database (DDB). In a distributed database system, an application can transparently operate the database, and the data in the database is stored in different local databases, managed by different DBMS, run on different machines, supported by different operating systems, and connected by different communication networks.

A distributed database is logically a unified whole: it is a single logical database in front of the user, and physically it is stored on different physical nodes. An application can access geographically distributed databases through a network connection.

Its distribution is manifested in the fact that the data in the database is not stored in the same site. More precisely, not stored on a storage device on the same computer. That's the difference from a centralized database.

From the user's point of view, a distributed database system is logically the same as a centralized database system, where users can execute global applications at any site. It's as if the data is stored on the same computer and managed by a single database management system (DBMS), and the user doesn't feel any different. Each database server in a distributed database cooperates to maintain the consistency of the global database.

A distributed database system is a client-server architecture.

Accurate distributed databases.

Definition: A distributed database is made up of a set of data that is distributed across different computers in a computer network, and each node in the network has the ability to process independently (called site autonomy) and can perform local applications. At the same time, each node can also execute the global model application through the network communication subsystem.

Compared with the previous definition, the emphasis is placed on site autonomy and collaboration between autonomous sites.

Distributed database system: A rough definition is "a distributed database consisting of a set of data that is physically distributed across different nodes (also known as sites) of a computer network and logically belongs to the same system." Two points are emphasized here:

1) Distribution: The data in the database is not stored in the same field, more precisely, not stored on the storage device of the same computer, which can be different from the centralized database.

2) Logical integrity: These data are logically interconnected and form a whole (logically like a centralized database).

Data distribution is a major feature of distributed databases. Implementing local waste of data access is an important part of distributed database design. In this paper, Tong Duan introduces the main characteristics and key technologies of the distributed database system, focusing on the relationship of subdivision and access to distributed data.

Distributed data processing uses a divide-and-conquer approach to solve large-scale data management problems, and the basic characteristics of the data it processes are as follows:

1. Transparent management of distribution.

In a distributed system, data is not stored on a single site, but on multiple sites on a computer network. But logically as a whole, they are shared by all users and managed by a single DBMS. Users don't need to indicate where the data is stored or which server in the distributed system does it.

2. Transparent management of replicated data.

Replication of distributed data helps improve performance and makes it easier to reconcile disparate and conflicting user requirements. At the same time, when one server fails, the data on this server is backed up on other servers, improving the availability of the system.

This multi-replica approach is transparent to users, that is, users do not need to know the existence of replicas, and the system manages and coordinates the invocation of replicas.

3. Reliability of transactions.

Distributed data processing has a repetitive composition, thus eliminating the problem of a single point of failure, i.e., a failure of one or more servers in a system to send a failure that does not bring down the entire system, thereby improving the reliability of the system.

However, in a distributed system, transactions are concurrent, that is, different users may access the same data source at the same time, which requires the system to support distributed concurrency control to ensure the consistency of data in the system.

Distributed systems can solve the problem of storing and accessing massive amounts of data, but in a distributed environment, databases will encounter more complex problems, such as the following:

Data is stored in multiple copies in a distributed environment, so how to choose one copy when providing users with access to data, or how to make each copy of the system be updated if the user modifies the data of one copy.

If a server fails due to network bandwidth or hardware or software functionality issues while all replicas of the system are being updated. In this case, how do you ensure that the replicas on this server are consistent with the other replicas when you recover from a failure?

Distributed data has different theoretical supports, and the top domestic data in the TiDB official community (asktug) can learn about TIDB

Horizontal elastic scaling.

By simply adding new nodes, you can horizontally expand TIDB and expand throughput or storage on demand, easily coping with high-concurrency and massive data scenarios.

Distributed transactions.

TiDB supports 100% of standard ACID transactions.

Truly financial-grade high availability.

Compared with the traditional master-slave (M-S) replication scheme, the RAFT-based majority election cancellation protocol can provide 100% strong data consistency guarantee at the financial level of the town, and can achieve auto-failover without manual intervention of Mieda Akira without losing most replicas.